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Conclusion

PM2.5 originating from speed-changing traffic modulates the autonomic control of the heart
rhythm, increases the frequency of premature supraventricular beats and elicits pro-inflammatory
and pro-thrombotic responses in healthy young men.

Background

Exposure to fine particulate matter (PM2.5) in the ambient air increases daily deaths [1] and hospitalization for cardiovascular diseases [2] in the U.S. and throughout the world [3] with most effects within one day after exposure. It is estimated that 800,000 excess
deaths worldwide each year may be attributable to particulate matter air pollution
[4], possibly secondary to myocardial infarction [5], life-threatening arrhythmias [6] or heart failure, as reviewed in a recent American Heart Association scientific statement
[7]. Yet, the underlying pathophysiological mechanisms that link PM2.5 and cardiopulmonary mortality are poorly understood.

Particles of motor vehicle origin appear to be especially potent with regard to increased
mortality [8,9] and hospital admissions due to cardiovascular diseases [10]. Vehicles represent a microenvironment with potentially high exposure to air pollutants
from mobile sources. We previously showed that occupational in-vehicle PM2.5 exposure to North Carolina Highway Patrol troopers was associated with changes in
cardiac parameters, blood proteins associated with inflammation, hemostasis and thrombosis,
and increased red blood cell volume (MCV) 10 to 15 hours after completing their shift
[11]. These findings were little affected by potential confounders. Controlling for estimates
of occupational stress even slightly improved the strength of association with some
cardiac parameters. In this paper, we investigated how these health endpoints were
associated with specific sources of PM2.5.

Results

Subjects

Data from nine male non-smoking troopers (8 Caucasian, 1 African-American) were used
for the analysis: ten participated, one was excluded due to very high numbers of ectopic
beats and high serum cholesterol. This left a total of 36 person-days with valid health
data. Their age ranged from 23 to 30 years (mean 27.3 years), their weight from 74
to 102 kg (87 kg), their height from 168 to 191 cm (179 cm), and their body mass index
from 24 to 31 kg/m2 (27 kg/m2). All were in excellent physical condition.

Exposure inside the cars and source identification

Elemental PM2.5-components and co-pollutants that were correlated to the PM2.5 measurements were included in the analysis, if they had over 75% of the data above
the reporting limit. Table 1 shows their in-vehicle concentrations and the correlations to the PM2.5 measurements. Data of 36 individual samples were available after correction of one
silicon-outlier, and replacement of three missing benzene and two missing aldehydes
values by their respective means. All concentrations measured were below current occupational
threshold limits.

Table 1. Components included in the analysis In-vehicle concentrations of the elemental components
of PM2.5 and gaseous co-pollutants included in the analysis (n = 36 samples): Arithmetic average,
standard deviation and correlation (Spearman-Rho) to PM2.5Mass and PM2.5Lightscatter. *) p < 0.05.

Figure 1.Source factors loadings. Factor loadings of the different components of the two models and the proposed sources
for these factors. Loadings large than 0.4 are highlighted in yellow.

Table 2. Source model characteristics Characteristics of the two models and their factors of
the principal factor analysis and their associations with the two PM2.5-measures: Model A includes all components shown in Table 1, Model B excludes Ca,
Cr, Se and W.

Source model B was calculated using only elements that were clearly correlated to
PM2.5 and with the majority of data more than 3 sigma above background noise (i.e., without
Ca, Cr, Se and W). Three source factors were identified (Figure 1B and Table 2). Factor 1 was dominated by silicon, aluminum, titanium and iron (named "road surface");
factor 2 by benzene and carbon monoxide ("gasoline"); and factor 3 by copper, sulfur
and aldehydes ("speed-change").

The factor "road surface" of Model B was significantly correlated to the factors "crustal"
and "steel wear" of Model A (R = 0.80 and 0.64, respectively). Factor "gasoline" of
A was correlated to factor "gasoline" of B (R = 0.80); and factor "speed-change" of
A to "speed-change" of B (R = 0.91). In contrast, the source factors within each model
were completely uncorrelated (R < 0.09).

Health endpoints associated with sources

The associations between health endpoints and source factors were studied in a multivariate
approach. Figure 2 shows the results for Model A; Figure 3 those for Model B (only health endpoints associated to one of the sources with p
< 0.05 are displayed). In both models, most of the significant health effect estimates
were associated with the "speed-change" factor (MCL, SDNN, PNN50, supraventricular
ectopic beats, % neutrophils, % lymphocytes, MCV, von Willebrand Factor, and protein
C). The association with MCV remained unchanged when controlled for osmolality. Two
significant associations were observed for the "crustal" factor of Model A (uric acid
and MCL), none for the "steel wear" and the "gasoline" factor of either model.

Strong heteroscedasticity (i.e., an indication for a violation of the underlying statistical
assumptions) was evidenced in the residual analysis of the models for red blood cell
count, hematocrit and hemoglobin (which were significantly associated with the "speed-change"
factor). However, every attempt to remove the heteroscedasticity by adjusting the
variance-covariance structure also completely removed the significance of these associations.

Control for potential confounders

The associations observed between factors and health parameters were tested for the
following potential confounders: Temperature, relative humidity, the number of law-enforcement
activities during the shift (as estimate of workload) and the average speed during
the shift. Controlling for these confounders had no visible effect on most effect
estimates of the "crustal" and the "speed-change" factor, and the associated health
parameters. However, in Models A and B, including these confounders altered the effect
estimates with blood urea nitrogen and vWF, especially including all confounders together
into the models lowered the effect estimates for the source factor "speed-change"
by about one fifth and the confidence interval included zero. In Model A, the estimate
for PNN50 was not altered by any of the confounders, but including all confounders
into the same model widened the confidence interval to include zero.

Discussion

We previously reported that in-vehicle exposure to PM2.5 was associated with increases in markers of inflammation and coagulation, and modulations
of heart rate variability in Highway Patrol troopers [11]. Here we demonstrate that most health endpoints were associated to a PM2.5 source factor that reflects speed-changing traffic conditions (dominated by copper,
aldehydes and sulfur). Under such driving conditions, copper reflects wear of brakes,
aldehydes reflect emissions from accelerating vehicles and sulfur reflects secondary
aerosols and possibly diesel combustion products.

In Model A, four principal factors of PM2.5-exposure inside the patrol cars were identified. Their loadings suggest the main
in-vehicle sources of PM2.5. Factor 1 reflects exposure to crustal material from the soils in the study region
and the road surface ("crustal" factor). Factor 2 represents wear and tear of mechanical
automotive parts, mostly chrome-titanium steels ("steel wear" factor). Factor 3 represents
components derived from gasoline combustion ("gasoline factor"). Finally, factor 4
is characterized by components expected from speed-changing traffic ("speed-change"
factor): copper from brakes [12] and aldehydes from engine emissions [13]. Note that photochemical processes [14] are an unlikely source for this factor, since urban background and roadside levels
near free-flowing traffic were much lower than in-vehicle levels [15]. The source of the high sulfur loading is unclear. Diesel combustion of accelerating
trucks would be a plausible source candidate. However, sulfur is ubiquitous on secondary
urban aerosols. It was the most concentrated element on PM2.5 in the study [15]. This prevents the identification of local sources with sulfur as a tracer. A cautious
interpretation might be that factor 4 reflects particles from speed-changing traffic
mixed with secondary urban particles; a mixture expected on roads in an urban-sprawl
area like Raleigh.

Model B proposes only three sources. However, they correspond in principle to the
sources from Model A, except that the factors " crustal" and "steel wear" seem to
be collapsed into a single source factor "road surface". This notion is supported
by the good correlation between the corresponding factors.

The average PM2.5 concentration of ca. 23 μg/m3 inside the vehicles was at a moderate level compared to the 24-hour National Ambient
Air Quality Standard for PM2.5 of 65 μg/m3. The two methods used to measure PM2.5 were highly correlated. The differences of their correlations to the components (Table
1) and to the source factors (Tables 2) reflect the fact that two different methods were used to assess the particle mass
[15]: PM2.5Lightscatter reflects mostly accumulation mode particles (0.2 to 2 μm), whereas PM2.5Mass includes some coarse dust including fine sand.

The "speed-change" factor (Models A and B) was significantly associated with increased
percentage of neutrophil leucocytes in the circulating blood, with decreased percentage
of lymphocytes, and with changes in markers of endothelial activation and hemostasis.
Endothelial cells are a major storage site for von Willebrand factor [16], and plasma levels of vWF serve as markers for endothelial activation [17]. Protein C is an antithrombotic agent, it is activated on the endothelium and reduced
in the blood after inflammatory stimulation due to protein C consumption [18]. Consequently, endothelial cells may be involved in both inflammatory and coagulatory
responses to traffic particles. Blood urea nitrogen was also associated with the "speed-change"
factor. This finding would be consistent with the postulated inflammation since blood
urea nitrogen increases several hours after an inflammatory stimulus (pig model) [19]. Blood urea nitrogen and vWF lost significance when controlled for all potential
confounders together, although the effect estimates were not much changed. It should
be noted that including this many confounders into a model with a relatively small
number of samples reduces the strength of the statistics considerably.

The "speed-change" factor was significantly associated with changes in MCV (independent
of osmolality) and similar to the association for PM2.5Lightscatter with MCV reported earlier [11]. The present analysis suggests that particles originating from speed-changing traffic
are an important source of this association with circulating red blood cell mean volume
(while other red blood cell indices were not affected). This is consistent with in-vitro
blood experiments, where high concentrations of particles caused dose-dependent hemolysis,
which was explained by oxidative damage to the membranes [20]. Note that MCV increases with increasing doses of hemolytic chemicals [21]. Future studies might answer the question whether particle-induced oxidative stress
caused the association observed between MCV and the "speed-change" source factor.

The heart beat interval MCL increased in association with the "crustal" factor and
the "speed-change" factor. Additionally, the "speed-change" factor was associated
with significant increases in heart rate variability (SDNN and PNN50) and frequency
of supraventricular ectopic beats. This cardiac response suggests increased vagal
tone mostly in response to "speed-change" traffic particles. Fluctuations in autonomic
tone have been associated with the triggering of atrial arrhythmias [22]. Such fluctuations might also help explain the reported association between air pollution
exposure and increases in arrhythmias in patients with an implanted cardioverter defibrillator
[6].

The concentrations of particles and components in this study were low. Direct systemic
effects seem therefore unlikely. However, the proposed endothelial activation could
provide a link to pathological processes and the associated increase in cardiovascular
morbidity and mortality [7], as follows: Once particles are deposited on the surfaces of the airways or alveoli,
toxic products can quickly leach out or be produced on the surface of the particles.
Given the small volume of surface liquid, this can result in high local concentrations.
Copper and other transition metals can cause oxidative stress [23] and have been associated with inflammatory lung injury in human subjects [24] as well as airway epithelial cell injury in vitro [25]. This oxidative stress might induce responses in the adjacent cells. In the alveolar
region, the distance to the capillary endothelium is about 100 nanometers. Liberation
of pro-thrombotic and pro-inflammatory mediators are well-described consequences of
oxidative stress to endothelial and other cells [26]. Inflammatory stimuli also might induce a vagal response [27]. The components copper, sulfur and aldehydes dominated the "speed-change" factor.
They seem to merit further attention in future targeted studies on particle toxicology.

Surprisingly, the "steel wear" factor of Model A was not associated to any inflammatory
markers, although metal content of particles has been reported to be associated with
inflammatory processes [24,25,28]. It would be interesting to study such wear particles with regard to size and solubility
of metals.

One limitation of this study is the fact that only the association between the mean
exposure during the evening shift and the response on the following morning was studied.
This design ensured that potential diurnal variations of exposure and health parameters
could not mimic a dose-response association, and that the exposure inside the cars
was followed by a long unexposed resting period. However, it cannot be excluded that
exposures and follow-up at other times of the day could have resulted in different
dose-response estimates. Another limitation is the study population, since the troopers
were a homogenous group of young, healthy, non-smoking people in excellent physical
condition. Consequently, it is possible that the relative response such as the %-increase
of inflammatory blood components or ectopic heart beats might be different in the
troopers as compared to what could be expected in the general population or in individuals
with elevated cardiovascular risks that have higher baseline levels. A final limitation
is that the source factor "speed-changing" traffic does not represent a single source
but rather a combination of closely related sources such as break wear and engine
exhaust products. Answering the question, which of these sub-sources was causing the
effects, would require a larger number of subjects or targeted toxicological studies.

Conclusions

Fine particulate matter from vehicular traffic may activate one or more signaling
pathways that cause pro-inflammatory, pro-thrombotic and hemolytic responses in healthy
young men. The changes in the heart rate variability suggest an increased parasympathetic
input to the heart with an associated increase in arrhythmic events, possibly in response
to mild lung inflammation. These findings suggest the hypothesis that pollutants emitted
during speed-changing traffic conditions negatively impact the health risks of professional
or otherwise frequent vehicle drivers and passengers, or other people exposed to these
particles. A long-term cardiovascular risk to the troopers can not be excluded, especially
when considering the reported increase in myocardial infarction among professional
drivers [29] and the increase in mortality among people living near major roadways [9]. These findings might be helpful for designing targeted studies in the future that
investigate causative pathways for health effects of PM2.5.

Methods

The study was conducted in fall 2001 in Wake County, North Carolina, USA. The Institutional
Review Board of the UNC School of Medicine approved the study. All subjects gave informed
written consent. Data from nine non-smoking male Highway Patrol troopers were analyzed.
Each was monitored from Monday to Thursday while working the 3 PM to midnight shift.
The troopers refrained from alcohol, caffeine and any medication from 24 hours before
the start until the end of their participation. Each patrol car was equipped with
air quality monitors to measure their exposure during the shift as described earlier
[15]. Particle mass was assessed by two methods: PM2.5Mass by weighing filters; and PM2.5Lightscatter based on lightscattering. "Aldehydes" refers to the sum of formaldehyde, acetaldehyde,
acrolein, propionaldehyde, crotonaldehyde, n-butyraldehyde, benzaldehyde, valeraldehyde,
tolualdehyde, hexanaldehyde, and 2,5-dimethylbenzaldehyde.

Health parameters were assessed by ambulatory electrocardiography during the work
shift and the subsequent sleep phase, and by analyses of peripheral blood samples
drawn 15 hours after completion of the shift as described earlier [11]. Heart rate variability (HRV) measures in the time and frequency domain were calculated
for resting periods before and after the shift, and in the morning after awakening.
For the analysis presented, only data from the morning resting period were used. Parameters
included the mean cycle length of normal R-R intervals (MCL), the standard deviation
of normal R-R intervals (SDNN) and the percentage of normal R-R interval differences
greater than 50 msec (PNN50), low frequency (0.04 to 0.15 Hz), high-frequency power
(0.15 to 0.40 Hz) and the ratio of low to high frequency power. The number of ventricular
and supraventricular ectopic beats were counted during the shift and the contiguous
night.

Statistical methods

For classification of the exposure by potential sources, a principal factor analysis
(factanal procedure) with variance-maximizing rotation [30] was conducted after controlling for outliers and missing data. One silicon value
measured inside a patrol car was an outlier, possibly due to a grain of sand. This
value was replaced by an estimate based on the aluminum level (Al was highly correlated
to Si). Missing data (3 values of benzene and 2 of aldehydes) were replaced by the
mean of the component concerned. For data below the propagated detection limit, machine-readouts
were used. Factors with sum of squares of factor loadings larger than one were retained.
The number of exposure variables included was limited to obtain stable results with
this relatively small number of individual samples: only variables with a clear association
to PM2.5 and with reasonable data quality were used. In a first model ("Model A") PM2.5-components, that were significantly correlated to either PM2.5Mass or PM2.5Lightscatter (Spearman Rho > 0.3), and gaseous co-pollutants, that were strongly and significantly
correlated (Rho > 0.5) were included in the source factor analysis if at least 75%
of the data were above reporting limit. A second model ("Model B") was calculated
to assess the robustness of the source factor modeling and the associated health effects.
Model B excluded PM2.5-components from the analysis with large uncertainties (Cr, Se and W with over 50%
of data less than 3 sigma above background noise) or with weak correlation to PM2.5 (Ca).

Mixed effects regression models with restricted maximum-likelihood estimation, exposure
factors as fixed effects and an unconstrained variance-covariance structure with subjects
as grouping factors were used to investigate the associations between exposure and
health endpoints [11,31]. Potential confounders were controlled for by including them into the models. Model
testing included alternative, constrained variance-covariance structures (first-order
autoregressive as well as linear and exponential spatial designs), and adding exposure
factors to the random effects structure. None of these attempts improved the overall
quality of the models judged by the Akaike Information Criterion, analysis of variance
and residual analysis (distribution and autocorrelation). Consequently, only results
from unconstrained models are reported.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MR conceived of and lead the study, collected the samples, performed the statistical
analysis and drafted the manuscript. RBD supervised the analyses of blood components
and elemental PM2.5-components. TRG supervised the on-site health assessment and the coordination of
the troopers. MCH analyzed the ambulatory electrocardiograms and calculated the HRV
statistics. PAB participated in the study management and in the layout of the manuscript.
RWW supervised the assessment of air pollutants. WEC evaluated the volunteering troopers,
and supervised the heart data analysis. All authors participated in the study design,
and reviewed and approved the final manuscript.

Acknowledgements

We thank the North Carolina State Highway Patrol for enabling this study, and the
support staff of NCSHP, UNC Chapel Hill, U.S. EPA, all contractors, and the participating
troopers.

This work has been funded by The United States Environmental Protection Agency under
Cooperative Agreements CR-824195 and CR-829522 to the University of North Carolina
at Chapel Hill, by contract 68-D-00-206 to ManTech Environmental Technology and by
the Swiss National Science Foundation to MR. It has been subjected to Agency review
and approved for publication but does not necessarily reflect EPA policy.